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O =t^j?+Virtual Memory Primitives for User Programs",+ " aAndrew Appel and Kai Li Princeton U. Appears in ASPLOS 1991 Presented by: Fabin E. Bustamante >aHa B MotivationRUses of virtual memory Traditional  increase size of address space by keeping frequently-accessed subset in physical mem. IPC through shared pages Guarantee reentrance making instruction-spaces read-only Zeroed-on-demand or copy-on-write portions of memory & Modern OS enable user-programs to play such tricks by allowing association of handlers w/ protection violations This work Look at examples of algorithms using user-level page-protection techniques Benchmarks today s (1991) OS support of such techniques Draw some lessons for OS implementations^PPzPPz  C VM PrimitivesOS VM services needed by some of these apps. TRAP  handle page-fault traps in user-mode PROT1  decrease accessibility of 1 page PROTN  same for N pages UNPROT  increase accessibility of 1 page DIRTY  return list of dirty pages since previous call MAP2  map same physical page at 2 != virtual addresses, at != levels of protections, in the same address PAGESIZE changes? PROT1 & PROTN  some OSs may support only one, but most apps do this for a batch (not commonly needed for UNPROT) DIRTY could be done in user-mode based on PROTN, TRAP and UNPROT but the OS can do it more efficiently Some apps want a thread to have access to a particular page but want others to fault on that page; this can be done w/ MAP2 6.KVyVDVM ApplicationsDA sample of applications which use VM primitives to draw general conclusions on what user programs require from the OS & HW Concurrent garbage collection Shared virtual memory Concurrent checkpointing Generational garbage collection Persistent stores Extending addressability Data-compression paging Heap overflow detection  |EHDVM Apps  Real-time, concurrent GCStop-and-copy GC Memory divided in 2 contiguous regions: from- & to-space At the beginning of a collections, all objects in from-space Collector, starting from registers & other global roots, traces out the graph of reachable objects, copying them to to-space When done, what s left in from-space is garbage At that point, roles of from- & to-space are reversed (flip) Mutator (app program) runs until to-space is full Forwarding  examining a pointer into from-space, copying the referenced object if necessary & updating the pointer Obvious problem  long delay while mutator is suspended Real-time GC  the mutator is never interrupted for longer than a very small constant time Baker s (RT, sequential) - Mutator sees only to-space pointers  Checking every pointer fetch requires HW support for efficiency Needs to be sequential to avoid conflicting access to object^ZZIZ}ZI}  IReal-time, concurrent GCInstead of checking every pointer fetched from mem., collector uses VM page protection Pages in un-scanned area are set to  no access When mutator tries to access an unscanned object, it gets a page-access trap Collector fields trap Scans objects in page, copying from-space ones & forwarding pointers as necessary Unprotects page & resume mutator Collector also runs concurrently scanning pages & unprotecting them as it goes (to ! mutator s page-access traps) Algorithm requires TRAP  to detect fetches from unscanned area PROTN  to mark the entire to-space inaccessible during flip UNPROT  needed as each page is scanned MAP2  to allow collector to scan it while mutator can t access it You may want to reduce page size, obviously PsPPPPsS " 1A Shared VM*Another use, implement shared VM on network of computers Basic idea  use paging mechanism to control & maintain single-writer/multiple-reader coherence at page level All nodes see a coherent shared memory address space, as big as allowed by MMU Read-only pages replicated in 1+ nodes Write-only only in one Mem. mapping manager see its local memory as a big cache of SVM address space Mem. reference may cause a page fault when page is not in node s physical mem. Algorithms requires TRAP, PROT1, UNPROT, MAP2 and maybe PAGESIZE FZ.ZZ.JConcurrent checkpointingIdea  use mechanisms to make checkpointing concurrent & real-time Instead of saving the writable main mem. to disk all at once Set all AS as  read only Restart program s threads A copying thread sequentially copy pages to a separate virtual AS as it goes When done copying, set AS back to read/write While program makes read memory references  no problem Write attempt to not-yet copied page Page fault, copying thread immediately copies page, sets access to read/write, & restarts faulting thread Algorithm requires TRAP, PROT1, PROTN, UNPROT, and DIRTY; a medium PAGESIZE may be appropriate^ZjZZLZjL  KGenerational GCAn efficient GC algorithm that depends on properties of dynamically allocated records in LISP & like languages Younger records are much more likely to die soon than older ones Younger records tend to point to older records Allocated records kept in distinct areas Gi of memory (generations) Records in Gi older than records in GI+1 Idea  used VM to detect pointers form older Gs to new ones DIRTY if available (GC examined dirty pages) Write protect older generations, The trap handler then save address on a list for the GC At GC time, the GC scans list for possible pointers into the youngest ones Algorithm requires DIRTY or TRAP, PROTN, and UNPROT Smaller PAGESIZE may be good as time for GC depends on page size <oPpPDP)P<PPPcPop)             - cLPersistent storesPersistent store  a dynamic allocation heap that persist bet/ program invocations Program execution may traverse, modify, commit and/or abort modifications Traversals should be as fast as for in-core data Done through VM  persistent store ~mem. mapped disk file However, permanent image of a modified object should not be modified until the commit GC can be used to improve efficiency, recovering pages, collocating related objects, etc Algorithm require TRAP and UNPROT, and file-mapping with copy-on-write If copy-on-write is not available, simulate it w/ PROTN, UNPROT and MAP2.P~P~MExtending addressability$Persistent store might grow > 232 objects; a problem for a 32-bit machine However, at any one run a program would probably access < 232 objects Idea  Use disk as second stage, disk pages use 64-bit When disk page brought to disk, translate addresses from 64-32b w/ a translation table Translation table per session Algorithm requires TRAP, UNPROT, PROT1 or PROTN In a multithreaded environment, MAP2ZZZBZ d B  NData-compression pagingBIn typical linked data structure, many words point to nearby objects, others are nil & basically, small entropy of the average word A GC can reduce it further by putting close-by objects that point to each other Idea  compress a page instead of paging it out (decompressing may be cheaper than page it in) Algorithm requires TRAP, PROT1 (or PROTN), UNPROT OS support (?) to determine when pages are not recently used.FZ\ZF\OHeap overflow detectionDProcess or thread s stack requires protection against overflow A well-known technique  mark pages above the top of the stack as invalid ! memory access will cause a page fault In most implementations of Unix  stack pages are not allocated until used, requires TRAP, PROTN, UNPROT Similar technique can be used in a garbage-collected system  here size of allocated region is commonly small ! performance is an issuenZZZ"{  n"@Usage of VM system servicesEVM primitive performanceTwo classes of algorithms Protect pages in large batches, upon each page-fault trap, unprotect 1 page Protect a page and unprotect it Since PROTN or PROT1, TRAP and UNPROT are always used together  measured them together (one is slow, everything is) Two microbenchmarks Sum of PROT1, TRAP, UNPROT x 100 Access a random protected page In fault handler, protect 1 other page, & unprotect faulting page Sum of PROTN, TRAP, UNPORT x 100 Protect 100 pages, access each in a random sequence In the fault handler unprotect faulting page Other measurements Time for a single instruction (ADD)  20-instrution loops w/ 18 ADDs Time for trap handler that does not change mem. protection Three OSs: Ultrix, SunOS, MACH 5 Archs: Sun 3/60, SparStn 1, DEC 3100, microVax 3m & i386 on iPSC/2 PlPP!PaP!PaPPPdPPl!  a  !ad  $$PMicrobenchmarks resultsFSystem design issuesLessons on hardware and OS design TLB consistency Many of the algorithms make memory less accessible in large batches & more accessible 1 at a time A good thing, specially in a multiprocessor When made less-accessible, outdated info in TLBs is dangerous  flush TLBs (shootdown  interrupt & request flushing) SW shootdown is expensive, but you can batch it Optimal page size Page size traditionally big given disk overhead, & For user-handled faults processed entirely in CPU, smaller is better Effect of varying page size can be done on HW w/ small page size  for PROT & UNPROT use small size, w/ disk use multi-page block"PPPPPP"  QSystem design issuesAccess to protected pages Many algorithms need way for user-mode service routine to access a page while client threads have no access Several ways to do this (illustrated w/ concurrent CG) Multiple mapping of same page at != addresses System call to copy memory to/from a protected area ($$$$ memory copies) Shared pages bet/ processes, collector running as a different process ($$ context switches) Collector running inside kernel  not the best place for GC Best  multiple mapping at some extra cost w/ two entries in page table for each physical page; potential of cache inconsistency (some mapping may be stale)VRSystem design issuesIs this too much to ask? Synchronous memory management algorithms may be problematic in highly pipelined machines (unless you got some hardware support) Instructions half way done with results written into registers Possible addressing-mode side-effects However, all but the heap overflow detection algorithm are sufficiently asynchronous ~ like the behavior of a traditional disk-pager from the machine s point of view Other primitives For persistent store w/ transaction  pin a page External-pager interface  the OS can tell the client which pages are LRU and about to be paged outee  G ConclusionsVM not just a tool for implementing large address spaces & protecting one user process from another Several algorithms rely on VM primitives & but these primitives haven t been paid enough attention Common traits of surveyed algorithms Mem. Is made less-accessible in large batches and more-accessible on at a time Fault-handling is done almost entirely by the CPU & take time proportional to the page size Page faults results in faulting page being made more accessible Frequency of faults is inversely related to locality of reference of the client program  so algorithms should scale well User-mode service routines need to access pages that are protected from user-mode client routines But don t need to examine client s CPU state FZZZ(   0` .T3f` T3f3f` 999MMM` lff3f3޲` eoHff33Ҷ` ff!` T3f3f___>?" dd@$?nPd@ d " @ ` n?" dd@   @@``PR   @ ` `(p>>  H@ (    s *( "0@  T Click to edit Master title style! !  c $ "0j  RClick to edit Master text styles Second level Third level Fourth level Fifth level!     S  Nff??"?  Nff??"? F  "  Nff??"  Nff??"   Nff??"  Nff??"?  c $ "`  b*(2Z  B޽h))? ? T3f3f___ 0___PPT10.[p1q___PPT92p22 aqualab01U   0  C(  ~  Hff??"A  0̍ "G  x ,   </x "  x T Click to edit Master title style! ! 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T3f3f___80___PPT10.H0<|rt&u( p}j0$.t(|8'1;9 wy?T~1Oh+'0Ux   , 8 D P\dWelcomeDistributed SystemsFabian E. Bustamante AquaLab05Fabian E. Bustamante64Microsoft PowerPoint@i/ @}@ OQ@PQCIqGSg  )'    """)))UUUMMMBBB999|PP3f333f3333f3ffffff3f̙3ff333f333333333f33333333f33f3ff3f3f3f3333f33̙33333f333333f3333f3ffffff3f33ff3f3f3f3fff3ffffffffff3ffff̙fff3fffff3fff333f3f3ff3ff33f̙̙3̙ff̙̙̙3f̙3f333f3333f3ffffff3f̙3f3f3f333f3333f3ffffff3f̙3f3ffffffffff!___www4'A x(xKʦ """)))UUUMMMBBB999|PP3f3333f333ff3fffff3f3f̙f3333f3333333333f3333333f3f33ff3f3f3f3333f3333333f3̙33333f333ff3ffffff3f33f3ff3f3f3ffff3fffffffff3fffffff3f̙ffff3ff333f3ff33fff33f3ff̙3f3f3333f333ff3fffff̙̙3̙f̙̙̙3f̙3f3f3333f333ff3fffff3f3f̙3ffffffffff!___wwweeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeϮϮϮϮϮϮϮϮϮϮϮϮϮϮϮϮϮϮϮϮ՜.+,0    On-screen ShowEast Texas Data Service&t Times New RomanTahomaArial WingdingsVerdana Helvetica aqualab01,Virtual Memory Primitives for User Programs MotivationVM PrimitivesVM Applications#VM Apps Real-time, concurrent GCReal-time, concurrent GC Shared VMConcurrent checkpointingGenerational GCPersistent storesExtending addressabilityData-compression pagingHeap overflow detectionUsage of VM system servicesVM primitive performanceMicrobenchmarks resultsSystem design issuesSystem design issuesSystem design issues Conclusions  Fonts UsedDesign Template Slide Titles,_~0Fabian E. BustamanteFabian E. Bustamante  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~     !"#$%&'()*+,-./012345679:;<=>?ABCDEFGLRoot EntrydO)Pictures2Current User@SummaryInformation( DUPowerPoint Document(M~DocumentSummaryInformation88